Generally, semantic communication with machines involves Natural Language Processing (NLP) and Natural Language Understanding (NLU), both of which use computer processing to fetch information from inputs, such as, human generated speech or text. The application of such technology involves processing speech or text queries in multi-modal conversational dialog applications. In a conversational dialog application, the components such as dialogue manager, automatic speech recognition engine, natural language understanding engine, and clients are available that conventionally perform their tasks. These conventional methods utilize the conversation engine and lack in identifying answers to sequential queries. Moreover, such methods do not identify the scope of main intent (i.e., theme), question type, entities, and their relationships in the conversation flow. Further, the ability to provide succinct responses or suggestions based on partial queries is missing in these conventional methods.
The conventional methods involve analyzing all possible answers to a query based on semantic analogy. Further, such methods do not make optimized usage of memory slots available to support conversation threads between the application and the user. This results in unnecessary utilization of a processor memory.